Romero Milà Blanca, Remakanthakurup Sindhu Kavyakantha, Mytinger John R, Shrey Daniel W, Lopour Beth A
Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States.
Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain.
Front Neurol. 2022 Jul 28;13:960454. doi: 10.3389/fneur.2022.960454. eCollection 2022.
Early diagnosis and treatment are critical for young children with infantile spasms (IS), as this maximizes the possibility of the best possible child-specific outcome. However, there are major barriers to achieving this, including high rates of misdiagnosis or failure to recognize the seizures, medication failure, and relapse. There are currently no validated tools to aid clinicians in assessing objective diagnostic criteria, predicting or measuring medication response, or predicting the likelihood of relapse. However, the pivotal role of EEG in the clinical management of IS has prompted many recent studies of potential EEG biomarkers of the disease. These include both visual EEG biomarkers based on human visual interpretation of the EEG and computational EEG biomarkers in which computers calculate quantitative features of the EEG. Here, we review the literature on both types of biomarkers, organized based on the application (diagnosis, treatment response, prediction, etc.). Visual biomarkers include the assessment of hypsarrhythmia, epileptiform discharges, fast oscillations, and the Burden of AmplitudeS and Epileptiform Discharges (BASED) score. Computational markers include EEG amplitude and power spectrum, entropy, functional connectivity, high frequency oscillations (HFOs), long-range temporal correlations, and phase-amplitude coupling. We also introduce each of the computational measures and provide representative examples. Finally, we highlight remaining gaps in the literature, describe practical guidelines for future biomarker discovery and validation studies, and discuss remaining roadblocks to clinical implementation, with the goal of facilitating future work in this critical area.
早期诊断和治疗对于患有婴儿痉挛症（IS）的幼儿至关重要，因为这能最大程度地实现针对儿童个体的最佳预后可能性。然而，实现这一目标存在重大障碍，包括误诊率高或未能识别癫痫发作、药物治疗失败以及复发。目前尚无经过验证的工具可协助临床医生评估客观诊断标准、预测或测量药物反应，或预测复发可能性。然而，脑电图（EEG）在IS临床管理中的关键作用促使近期开展了许多关于该疾病潜在EEG生物标志物的研究。这些生物标志物包括基于人类对EEG视觉解读的视觉EEG生物标志物以及计算机计算EEG定量特征的计算EEG生物标志物。在此，我们根据应用（诊断、治疗反应、预测等）对这两类生物标志物的相关文献进行综述。视觉生物标志物包括对高峰失律、癫痫样放电、快速振荡以及癫痫样放电幅度和负担（BASED）评分的评估。计算标志物包括EEG幅度和功率谱、熵、功能连接性、高频振荡（HFOs）、长程时间相关性以及相位 - 幅度耦合。我们还介绍了每种计算方法并提供了代表性示例。最后，我们强调了文献中尚存的差距，描述了未来生物标志物发现和验证研究的实用指南，并讨论了临床实施中尚存的障碍，旨在推动这一关键领域的未来工作。
